Charting by machines

• 成果类型: Article
• 署名作者: Murray, Scott; Xia, Yusen; Xiao, Houping
• 署名单位: University System of Georgia; Georgia State University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2024.103791
• 发表日期: 2024
• 关键词: EFFICIENT MARKET HYPOTHESIS; Machine Learning; Deep learning; Charting; Technical analysis; Cross-section of stock returns

摘要

We test the efficient market hypothesis by using machine learning to forecast stock returns from historical performance. These forecasts strongly predict the cross-section of future stock returns. The predictive power holds in most subperiods and is strong among the largest 500 stocks. The forecasting function has important nonlinearities and interactions, is remarkably stable through time, and captures effects distinct from momentum, reversal, and extant technical signals. These findings question the efficient market hypothesis and indicate that technical analysis and charting have merit. We also demonstrate that machine learning models that perform well in optimization continue to perform well out -of -sample.